Overview

Dataset statistics

Number of variables25
Number of observations400
Missing cells1012
Missing cells (%)10.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory329.9 KiB
Average record size in memory844.6 B

Variable types

Numeric13
Categorical7
Boolean5

Warnings

bu is highly correlated with sc and 3 other fieldsHigh correlation
sc is highly correlated with bu and 1 other fieldsHigh correlation
sod is highly correlated with scHigh correlation
hemo is highly correlated with bu and 2 other fieldsHigh correlation
pcv is highly correlated with bu and 2 other fieldsHigh correlation
rbcc is highly correlated with bu and 2 other fieldsHigh correlation
bu is highly correlated with sc and 3 other fieldsHigh correlation
sc is highly correlated with bu and 3 other fieldsHigh correlation
sod is highly correlated with hemo and 1 other fieldsHigh correlation
hemo is highly correlated with bu and 4 other fieldsHigh correlation
pcv is highly correlated with bu and 4 other fieldsHigh correlation
rbcc is highly correlated with bu and 3 other fieldsHigh correlation
bu is highly correlated with scHigh correlation
sc is highly correlated with bu and 2 other fieldsHigh correlation
hemo is highly correlated with sc and 2 other fieldsHigh correlation
pcv is highly correlated with sc and 2 other fieldsHigh correlation
rbcc is highly correlated with hemo and 1 other fieldsHigh correlation
ane is highly correlated with pcv and 4 other fieldsHigh correlation
class is highly correlated with pcv and 14 other fieldsHigh correlation
pot is highly correlated with sc and 1 other fieldsHigh correlation
pcv is highly correlated with ane and 14 other fieldsHigh correlation
rbc is highly correlated with class and 4 other fieldsHigh correlation
bp is highly correlated with classHigh correlation
age is highly correlated with htnHigh correlation
su is highly correlated with class and 4 other fieldsHigh correlation
appet is highly correlated with class and 6 other fieldsHigh correlation
rbcc is highly correlated with ane and 10 other fieldsHigh correlation
sod is highly correlated with pcv and 4 other fieldsHigh correlation
sc is highly correlated with pot and 4 other fieldsHigh correlation
dm is highly correlated with class and 7 other fieldsHigh correlation
cad is highly correlated with suHigh correlation
al is highly correlated with class and 13 other fieldsHigh correlation
sg is highly correlated with class and 3 other fieldsHigh correlation
pc is highly correlated with class and 10 other fieldsHigh correlation
pcc is highly correlated with al and 1 other fieldsHigh correlation
bu is highly correlated with ane and 9 other fieldsHigh correlation
hemo is highly correlated with ane and 14 other fieldsHigh correlation
bgr is highly correlated with class and 5 other fieldsHigh correlation
ba is highly correlated with alHigh correlation
htn is highly correlated with ane and 13 other fieldsHigh correlation
pe is highly correlated with class and 6 other fieldsHigh correlation
dm is highly correlated with class and 1 other fieldsHigh correlation
class is highly correlated with dm and 3 other fieldsHigh correlation
sg is highly correlated with classHigh correlation
pcc is highly correlated with pcHigh correlation
pc is highly correlated with pccHigh correlation
rbc is highly correlated with classHigh correlation
htn is highly correlated with dm and 1 other fieldsHigh correlation
age has 9 (2.2%) missing values Missing
bp has 12 (3.0%) missing values Missing
sg has 47 (11.8%) missing values Missing
al has 46 (11.5%) missing values Missing
su has 49 (12.2%) missing values Missing
rbc has 152 (38.0%) missing values Missing
pc has 65 (16.2%) missing values Missing
bgr has 44 (11.0%) missing values Missing
bu has 19 (4.8%) missing values Missing
sc has 17 (4.2%) missing values Missing
sod has 87 (21.8%) missing values Missing
pot has 88 (22.0%) missing values Missing
hemo has 52 (13.0%) missing values Missing
pcv has 71 (17.8%) missing values Missing
wbccc has 106 (26.5%) missing values Missing
rbcc has 131 (32.8%) missing values Missing
al has 199 (49.8%) zeros Zeros
su has 290 (72.5%) zeros Zeros

Reproduction

Analysis started2021-06-05 07:16:24.743089
Analysis finished2021-06-05 07:16:54.896875
Duration30.15 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

age
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct76
Distinct (%)19.4%
Missing9
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean51.48337596
Minimum2
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2021-06-05T12:46:55.272863image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile19
Q142
median55
Q364.5
95-th percentile74.5
Maximum90
Range88
Interquartile range (IQR)22.5

Descriptive statistics

Standard deviation17.16971409
Coefficient of variation (CV)0.3335001594
Kurtosis0.0578404946
Mean51.48337596
Median Absolute Deviation (MAD)10
Skewness-0.6682594692
Sum20130
Variance294.7990819
MonotonicityNot monotonic
2021-06-05T12:46:55.416481image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6019
 
4.8%
6517
 
4.2%
4812
 
3.0%
5012
 
3.0%
5512
 
3.0%
4711
 
2.8%
6210
 
2.5%
5610
 
2.5%
4510
 
2.5%
5410
 
2.5%
Other values (66)268
67.0%
ValueCountFrequency (%)
21
 
0.2%
31
 
0.2%
41
 
0.2%
52
0.5%
61
 
0.2%
71
 
0.2%
83
0.8%
111
 
0.2%
122
0.5%
141
 
0.2%
ValueCountFrequency (%)
901
 
0.2%
831
 
0.2%
821
 
0.2%
811
 
0.2%
804
1.0%
791
 
0.2%
781
 
0.2%
765
1.2%
755
1.2%
743
0.8%

bp
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct10
Distinct (%)2.6%
Missing12
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean76.46907216
Minimum50
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2021-06-05T12:46:55.547132image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile60
Q170
median80
Q380
95-th percentile100
Maximum180
Range130
Interquartile range (IQR)10

Descriptive statistics

Standard deviation13.68363749
Coefficient of variation (CV)0.1789434226
Kurtosis8.646095189
Mean76.46907216
Median Absolute Deviation (MAD)10
Skewness1.605428957
Sum29670
Variance187.2419351
MonotonicityNot monotonic
2021-06-05T12:46:55.673793image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
80116
29.0%
70112
28.0%
6071
17.8%
9053
13.2%
10025
 
6.2%
505
 
1.2%
1103
 
0.8%
1201
 
0.2%
1801
 
0.2%
1401
 
0.2%
(Missing)12
 
3.0%
ValueCountFrequency (%)
505
 
1.2%
6071
17.8%
70112
28.0%
80116
29.0%
9053
13.2%
10025
 
6.2%
1103
 
0.8%
1201
 
0.2%
1401
 
0.2%
1801
 
0.2%
ValueCountFrequency (%)
1801
 
0.2%
1401
 
0.2%
1201
 
0.2%
1103
 
0.8%
10025
 
6.2%
9053
13.2%
80116
29.0%
70112
28.0%
6071
17.8%
505
 
1.2%

sg
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5
Distinct (%)1.4%
Missing47
Missing (%)11.8%
Memory size23.1 KiB
1.02
106 
1.01
84 
1.025
81 
1.015
75 
1.005
 
7

Length

Max length5
Median length4
Mean length4.461756374
Min length4

Characters and Unicode

Total characters1575
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.02
2nd row1.02
3rd row1.01
4th row1.005
5th row1.01

Common Values

ValueCountFrequency (%)
1.02106
26.5%
1.0184
21.0%
1.02581
20.2%
1.01575
18.8%
1.0057
 
1.8%
(Missing)47
11.8%

Length

2021-06-05T12:46:55.925157image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-05T12:46:56.014879image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.02106
30.0%
1.0184
23.8%
1.02581
22.9%
1.01575
21.2%
1.0057
 
2.0%

Most occurring characters

ValueCountFrequency (%)
1512
32.5%
0360
22.9%
.353
22.4%
2187
 
11.9%
5163
 
10.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1222
77.6%
Other Punctuation353
 
22.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1512
41.9%
0360
29.5%
2187
 
15.3%
5163
 
13.3%
Other Punctuation
ValueCountFrequency (%)
.353
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1575
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1512
32.5%
0360
22.9%
.353
22.4%
2187
 
11.9%
5163
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1575
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1512
32.5%
0360
22.9%
.353
22.4%
2187
 
11.9%
5163
 
10.3%

al
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct6
Distinct (%)1.7%
Missing46
Missing (%)11.5%
Infinite0
Infinite (%)0.0%
Mean1.016949153
Minimum0
Maximum5
Zeros199
Zeros (%)49.8%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2021-06-05T12:46:56.123588image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.352678913
Coefficient of variation (CV)1.330134264
Kurtosis-0.3833766021
Mean1.016949153
Median Absolute Deviation (MAD)0
Skewness0.9981572421
Sum360
Variance1.829740241
MonotonicityNot monotonic
2021-06-05T12:46:56.234292image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0199
49.8%
144
 
11.0%
343
 
10.8%
243
 
10.8%
424
 
6.0%
51
 
0.2%
(Missing)46
 
11.5%
ValueCountFrequency (%)
0199
49.8%
144
 
11.0%
243
 
10.8%
343
 
10.8%
424
 
6.0%
51
 
0.2%
ValueCountFrequency (%)
51
 
0.2%
424
 
6.0%
343
 
10.8%
243
 
10.8%
144
 
11.0%
0199
49.8%

su
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct6
Distinct (%)1.7%
Missing49
Missing (%)12.2%
Infinite0
Infinite (%)0.0%
Mean0.4501424501
Minimum0
Maximum5
Zeros290
Zeros (%)72.5%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2021-06-05T12:46:56.342004image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.099191252
Coefficient of variation (CV)2.441874237
Kurtosis5.055348003
Mean0.4501424501
Median Absolute Deviation (MAD)0
Skewness2.464261823
Sum158
Variance1.208221408
MonotonicityNot monotonic
2021-06-05T12:46:56.447722image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0290
72.5%
218
 
4.5%
314
 
3.5%
113
 
3.2%
413
 
3.2%
53
 
0.8%
(Missing)49
 
12.2%
ValueCountFrequency (%)
0290
72.5%
113
 
3.2%
218
 
4.5%
314
 
3.5%
413
 
3.2%
53
 
0.8%
ValueCountFrequency (%)
53
 
0.8%
413
 
3.2%
314
 
3.5%
218
 
4.5%
113
 
3.2%
0290
72.5%

rbc
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.8%
Missing152
Missing (%)38.0%
Memory size20.2 KiB
normal
201 
abnormal
47 

Length

Max length8
Median length6
Mean length6.379032258
Min length6

Characters and Unicode

Total characters1582
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownormal
2nd rownormal
3rd rownormal
4th rownormal
5th rownormal

Common Values

ValueCountFrequency (%)
normal201
50.2%
abnormal47
 
11.8%
(Missing)152
38.0%

Length

2021-06-05T12:46:56.721989image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-05T12:46:56.820724image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
normal201
81.0%
abnormal47
 
19.0%

Most occurring characters

ValueCountFrequency (%)
a295
18.6%
n248
15.7%
o248
15.7%
r248
15.7%
m248
15.7%
l248
15.7%
b47
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1582
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a295
18.6%
n248
15.7%
o248
15.7%
r248
15.7%
m248
15.7%
l248
15.7%
b47
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1582
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a295
18.6%
n248
15.7%
o248
15.7%
r248
15.7%
m248
15.7%
l248
15.7%
b47
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1582
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a295
18.6%
n248
15.7%
o248
15.7%
r248
15.7%
m248
15.7%
l248
15.7%
b47
 
3.0%

pc
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.6%
Missing65
Missing (%)16.2%
Memory size22.9 KiB
normal
259 
abnormal
76 

Length

Max length8
Median length6
Mean length6.453731343
Min length6

Characters and Unicode

Total characters2162
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownormal
2nd rownormal
3rd rownormal
4th rowabnormal
5th rownormal

Common Values

ValueCountFrequency (%)
normal259
64.8%
abnormal76
 
19.0%
(Missing)65
 
16.2%

Length

2021-06-05T12:46:57.071057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-05T12:46:57.169793image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
normal259
77.3%
abnormal76
 
22.7%

Most occurring characters

ValueCountFrequency (%)
a411
19.0%
n335
15.5%
o335
15.5%
r335
15.5%
m335
15.5%
l335
15.5%
b76
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2162
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a411
19.0%
n335
15.5%
o335
15.5%
r335
15.5%
m335
15.5%
l335
15.5%
b76
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Latin2162
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a411
19.0%
n335
15.5%
o335
15.5%
r335
15.5%
m335
15.5%
l335
15.5%
b76
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII2162
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a411
19.0%
n335
15.5%
o335
15.5%
r335
15.5%
m335
15.5%
l335
15.5%
b76
 
3.5%

pcc
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.5%
Missing4
Missing (%)1.0%
Memory size26.0 KiB
notpresent
354 
present
42 

Length

Max length10
Median length10
Mean length9.681818182
Min length7

Characters and Unicode

Total characters3834
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownotpresent
2nd rownotpresent
3rd rownotpresent
4th rowpresent
5th rownotpresent

Common Values

ValueCountFrequency (%)
notpresent354
88.5%
present42
 
10.5%
(Missing)4
 
1.0%

Length

2021-06-05T12:46:57.410150image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-05T12:46:57.505894image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
notpresent354
89.4%
present42
 
10.6%

Most occurring characters

ValueCountFrequency (%)
e792
20.7%
n750
19.6%
t750
19.6%
p396
10.3%
r396
10.3%
s396
10.3%
o354
9.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3834
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e792
20.7%
n750
19.6%
t750
19.6%
p396
10.3%
r396
10.3%
s396
10.3%
o354
9.2%

Most occurring scripts

ValueCountFrequency (%)
Latin3834
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e792
20.7%
n750
19.6%
t750
19.6%
p396
10.3%
r396
10.3%
s396
10.3%
o354
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII3834
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e792
20.7%
n750
19.6%
t750
19.6%
p396
10.3%
r396
10.3%
s396
10.3%
o354
9.2%

ba
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.5%
Missing4
Missing (%)1.0%
Memory size26.1 KiB
notpresent
374 
present
 
22

Length

Max length10
Median length10
Mean length9.833333333
Min length7

Characters and Unicode

Total characters3894
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownotpresent
2nd rownotpresent
3rd rownotpresent
4th rownotpresent
5th rownotpresent

Common Values

ValueCountFrequency (%)
notpresent374
93.5%
present22
 
5.5%
(Missing)4
 
1.0%

Length

2021-06-05T12:46:57.746251image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-05T12:46:57.841996image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
notpresent374
94.4%
present22
 
5.6%

Most occurring characters

ValueCountFrequency (%)
e792
20.3%
n770
19.8%
t770
19.8%
p396
10.2%
r396
10.2%
s396
10.2%
o374
9.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3894
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e792
20.3%
n770
19.8%
t770
19.8%
p396
10.2%
r396
10.2%
s396
10.2%
o374
9.6%

Most occurring scripts

ValueCountFrequency (%)
Latin3894
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e792
20.3%
n770
19.8%
t770
19.8%
p396
10.2%
r396
10.2%
s396
10.2%
o374
9.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII3894
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e792
20.3%
n770
19.8%
t770
19.8%
p396
10.2%
r396
10.2%
s396
10.2%
o374
9.6%

bgr
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct146
Distinct (%)41.0%
Missing44
Missing (%)11.0%
Infinite0
Infinite (%)0.0%
Mean148.0365169
Minimum22
Maximum490
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2021-06-05T12:46:57.945719image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile78.75
Q199
median121
Q3163
95-th percentile307.25
Maximum490
Range468
Interquartile range (IQR)64

Descriptive statistics

Standard deviation79.28171424
Coefficient of variation (CV)0.5355551179
Kurtosis4.225593588
Mean148.0365169
Median Absolute Deviation (MAD)25
Skewness2.010773173
Sum52701
Variance6285.590212
MonotonicityNot monotonic
2021-06-05T12:46:58.108284image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9910
 
2.5%
939
 
2.2%
1009
 
2.2%
1078
 
2.0%
1316
 
1.5%
926
 
1.5%
1096
 
1.5%
1406
 
1.5%
1306
 
1.5%
1176
 
1.5%
Other values (136)284
71.0%
(Missing)44
 
11.0%
ValueCountFrequency (%)
221
 
0.2%
705
1.2%
743
0.8%
752
 
0.5%
764
1.0%
783
0.8%
793
0.8%
802
 
0.5%
813
0.8%
823
0.8%
ValueCountFrequency (%)
4902
0.5%
4631
0.2%
4471
0.2%
4251
0.2%
4242
0.5%
4231
0.2%
4151
0.2%
4101
0.2%
3801
0.2%
3602
0.5%

bu
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct118
Distinct (%)31.0%
Missing19
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean57.42572178
Minimum1.5
Maximum391
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2021-06-05T12:46:58.499237image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile17
Q127
median42
Q366
95-th percentile162
Maximum391
Range389.5
Interquartile range (IQR)39

Descriptive statistics

Standard deviation50.50300585
Coefficient of variation (CV)0.8794492133
Kurtosis9.345288576
Mean57.42572178
Median Absolute Deviation (MAD)16
Skewness2.634374459
Sum21879.2
Variance2550.5536
MonotonicityNot monotonic
2021-06-05T12:46:58.649834image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4615
 
3.8%
2513
 
3.2%
1911
 
2.8%
4010
 
2.5%
189
 
2.2%
159
 
2.2%
489
 
2.2%
509
 
2.2%
328
 
2.0%
268
 
2.0%
Other values (108)280
70.0%
(Missing)19
 
4.8%
ValueCountFrequency (%)
1.51
 
0.2%
102
 
0.5%
159
2.2%
167
1.8%
177
1.8%
189
2.2%
1911
2.8%
207
1.8%
211
 
0.2%
226
1.5%
ValueCountFrequency (%)
3911
0.2%
3221
0.2%
3091
0.2%
2411
0.2%
2351
0.2%
2231
0.2%
2191
0.2%
2171
0.2%
2151
0.2%
2081
0.2%

sc
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct84
Distinct (%)21.9%
Missing17
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean3.072454308
Minimum0.4
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2021-06-05T12:46:58.809407image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile0.5
Q10.9
median1.3
Q32.8
95-th percentile11.89
Maximum76
Range75.6
Interquartile range (IQR)1.9

Descriptive statistics

Standard deviation5.741126067
Coefficient of variation (CV)1.868579803
Kurtosis79.30434545
Mean3.072454308
Median Absolute Deviation (MAD)0.6
Skewness7.509538252
Sum1176.75
Variance32.96052852
MonotonicityNot monotonic
2021-06-05T12:46:58.950033image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.240
 
10.0%
1.124
 
6.0%
123
 
5.8%
0.523
 
5.8%
0.722
 
5.5%
0.922
 
5.5%
0.618
 
4.5%
0.817
 
4.2%
2.210
 
2.5%
1.79
 
2.2%
Other values (74)175
43.8%
(Missing)17
 
4.2%
ValueCountFrequency (%)
0.41
 
0.2%
0.523
5.8%
0.618
4.5%
0.722
5.5%
0.817
4.2%
0.922
5.5%
123
5.8%
1.124
6.0%
1.240
10.0%
1.38
 
2.0%
ValueCountFrequency (%)
761
0.2%
48.11
0.2%
321
0.2%
241
0.2%
18.11
0.2%
181
0.2%
16.91
0.2%
16.41
0.2%
15.21
0.2%
151
0.2%

sod
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct34
Distinct (%)10.9%
Missing87
Missing (%)21.8%
Infinite0
Infinite (%)0.0%
Mean137.528754
Minimum4.5
Maximum163
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2021-06-05T12:46:59.107656image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum4.5
5-th percentile125
Q1135
median138
Q3142
95-th percentile150
Maximum163
Range158.5
Interquartile range (IQR)7

Descriptive statistics

Standard deviation10.40875205
Coefficient of variation (CV)0.07568418785
Kurtosis85.53436962
Mean137.528754
Median Absolute Deviation (MAD)3
Skewness-6.996568561
Sum43046.5
Variance108.3421193
MonotonicityNot monotonic
2021-06-05T12:46:59.240292image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
13540
10.0%
14025
 
6.2%
14122
 
5.5%
13921
 
5.2%
14220
 
5.0%
13820
 
5.0%
13719
 
4.8%
15017
 
4.2%
13617
 
4.2%
14713
 
3.2%
Other values (24)99
24.8%
(Missing)87
21.8%
ValueCountFrequency (%)
4.51
 
0.2%
1041
 
0.2%
1111
 
0.2%
1132
0.5%
1142
0.5%
1151
 
0.2%
1202
0.5%
1222
0.5%
1243
0.8%
1252
0.5%
ValueCountFrequency (%)
1631
 
0.2%
15017
4.2%
14713
3.2%
14610
 
2.5%
14511
2.8%
1449
 
2.2%
1434
 
1.0%
14220
5.0%
14122
5.5%
14025
6.2%

pot
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct40
Distinct (%)12.8%
Missing88
Missing (%)22.0%
Infinite0
Infinite (%)0.0%
Mean4.62724359
Minimum2.5
Maximum47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2021-06-05T12:46:59.389899image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2.5
5-th percentile3.4
Q13.8
median4.4
Q34.9
95-th percentile5.7
Maximum47
Range44.5
Interquartile range (IQR)1.1

Descriptive statistics

Standard deviation3.193904177
Coefficient of variation (CV)0.6902390407
Kurtosis142.5059115
Mean4.62724359
Median Absolute Deviation (MAD)0.5
Skewness11.58295556
Sum1443.7
Variance10.20102389
MonotonicityNot monotonic
2021-06-05T12:46:59.514524image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
530
 
7.5%
3.530
 
7.5%
4.927
 
6.8%
4.717
 
4.2%
4.816
 
4.0%
4.414
 
3.5%
3.814
 
3.5%
4.214
 
3.5%
4.114
 
3.5%
3.914
 
3.5%
Other values (30)122
30.5%
(Missing)88
22.0%
ValueCountFrequency (%)
2.52
 
0.5%
2.71
 
0.2%
2.81
 
0.2%
2.93
 
0.8%
32
 
0.5%
3.23
 
0.8%
3.33
 
0.8%
3.45
 
1.2%
3.530
7.5%
3.68
 
2.0%
ValueCountFrequency (%)
471
 
0.2%
391
 
0.2%
7.61
 
0.2%
6.61
 
0.2%
6.52
0.5%
6.41
 
0.2%
6.33
0.8%
5.92
0.5%
5.82
0.5%
5.74
1.0%

hemo
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct115
Distinct (%)33.0%
Missing52
Missing (%)13.0%
Infinite0
Infinite (%)0.0%
Mean12.52643678
Minimum3.1
Maximum17.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2021-06-05T12:46:59.665121image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum3.1
5-th percentile7.9
Q110.3
median12.65
Q315
95-th percentile16.9
Maximum17.8
Range14.7
Interquartile range (IQR)4.7

Descriptive statistics

Standard deviation2.912586609
Coefficient of variation (CV)0.2325151725
Kurtosis-0.4713980437
Mean12.52643678
Median Absolute Deviation (MAD)2.35
Skewness-0.3350946792
Sum4359.2
Variance8.483160754
MonotonicityNot monotonic
2021-06-05T12:46:59.825692image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1516
 
4.0%
10.98
 
2.0%
137
 
1.8%
13.67
 
1.8%
9.87
 
1.8%
11.17
 
1.8%
126
 
1.5%
13.96
 
1.5%
11.36
 
1.5%
10.36
 
1.5%
Other values (105)272
68.0%
(Missing)52
 
13.0%
ValueCountFrequency (%)
3.11
0.2%
4.81
0.2%
5.51
0.2%
5.61
0.2%
5.81
0.2%
62
0.5%
6.11
0.2%
6.21
0.2%
6.31
0.2%
6.61
0.2%
ValueCountFrequency (%)
17.83
0.8%
17.71
 
0.2%
17.61
 
0.2%
17.51
 
0.2%
17.42
0.5%
17.31
 
0.2%
17.22
0.5%
17.12
0.5%
174
1.0%
16.92
0.5%

pcv
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct42
Distinct (%)12.8%
Missing71
Missing (%)17.8%
Infinite0
Infinite (%)0.0%
Mean38.88449848
Minimum9
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2021-06-05T12:47:00.002220image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile24
Q132
median40
Q345
95-th percentile52
Maximum54
Range45
Interquartile range (IQR)13

Descriptive statistics

Standard deviation8.990104815
Coefficient of variation (CV)0.2312002254
Kurtosis-0.3205616838
Mean38.88449848
Median Absolute Deviation (MAD)7
Skewness-0.4336785974
Sum12793
Variance80.82198458
MonotonicityNot monotonic
2021-06-05T12:47:00.146833image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
4121
 
5.2%
5221
 
5.2%
4819
 
4.8%
4419
 
4.8%
4016
 
4.0%
4315
 
3.8%
4513
 
3.2%
4213
 
3.2%
5012
 
3.0%
2812
 
3.0%
Other values (32)168
42.0%
(Missing)71
17.8%
ValueCountFrequency (%)
91
 
0.2%
141
 
0.2%
151
 
0.2%
161
 
0.2%
171
 
0.2%
181
 
0.2%
192
0.5%
201
 
0.2%
211
 
0.2%
223
0.8%
ValueCountFrequency (%)
544
 
1.0%
534
 
1.0%
5221
5.2%
514
 
1.0%
5012
3.0%
494
 
1.0%
4819
4.8%
474
 
1.0%
469
2.2%
4513
3.2%

wbccc
Real number (ℝ≥0)

MISSING

Distinct89
Distinct (%)30.3%
Missing106
Missing (%)26.5%
Infinite0
Infinite (%)0.0%
Mean8406.122449
Minimum2200
Maximum26400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2021-06-05T12:47:00.291446image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2200
5-th percentile4500
Q16500
median8000
Q39800
95-th percentile12940
Maximum26400
Range24200
Interquartile range (IQR)3300

Descriptive statistics

Standard deviation2944.47419
Coefficient of variation (CV)0.3502773375
Kurtosis6.150639815
Mean8406.122449
Median Absolute Deviation (MAD)1700
Skewness1.621589372
Sum2471400
Variance8669928.258
MonotonicityNot monotonic
2021-06-05T12:47:00.454012image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
980011
 
2.8%
670010
 
2.5%
72009
 
2.2%
92009
 
2.2%
96009
 
2.2%
69008
 
2.0%
110008
 
2.0%
58008
 
2.0%
70007
 
1.8%
78007
 
1.8%
Other values (79)208
52.0%
(Missing)106
26.5%
ValueCountFrequency (%)
22001
 
0.2%
26001
 
0.2%
38002
 
0.5%
41001
 
0.2%
42003
0.8%
43006
1.5%
45003
0.8%
47004
1.0%
49001
 
0.2%
50005
1.2%
ValueCountFrequency (%)
264001
0.2%
216001
0.2%
191001
0.2%
189001
0.2%
167001
0.2%
163001
0.2%
157001
0.2%
152002
0.5%
149001
0.2%
146002
0.5%

rbcc
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct45
Distinct (%)16.7%
Missing131
Missing (%)32.8%
Infinite0
Infinite (%)0.0%
Mean4.707434944
Minimum2.1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2021-06-05T12:47:00.611591image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2.1
5-th percentile2.94
Q13.9
median4.8
Q35.4
95-th percentile6.3
Maximum8
Range5.9
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation1.025323266
Coefficient of variation (CV)0.217809333
Kurtosis-0.2700424813
Mean4.707434944
Median Absolute Deviation (MAD)0.7
Skewness-0.1833293208
Sum1266.3
Variance1.051287799
MonotonicityNot monotonic
2021-06-05T12:47:00.768172image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
5.218
 
4.5%
4.516
 
4.0%
4.914
 
3.5%
4.711
 
2.8%
4.810
 
2.5%
510
 
2.5%
3.910
 
2.5%
4.69
 
2.2%
3.49
 
2.2%
5.98
 
2.0%
Other values (35)154
38.5%
(Missing)131
32.8%
ValueCountFrequency (%)
2.12
0.5%
2.31
 
0.2%
2.41
 
0.2%
2.52
0.5%
2.62
0.5%
2.72
0.5%
2.82
0.5%
2.92
0.5%
33
0.8%
3.12
0.5%
ValueCountFrequency (%)
81
 
0.2%
6.55
1.2%
6.45
1.2%
6.34
1.0%
6.25
1.2%
6.18
2.0%
64
1.0%
5.98
2.0%
5.87
1.8%
5.75
1.2%

htn
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.5%
Missing2
Missing (%)0.5%
Memory size928.0 B
False
251 
True
147 
(Missing)
 
2
ValueCountFrequency (%)
False251
62.7%
True147
36.8%
(Missing)2
 
0.5%
2021-06-05T12:47:00.886855image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

dm
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.5%
Missing2
Missing (%)0.5%
Memory size928.0 B
False
261 
True
137 
(Missing)
 
2
ValueCountFrequency (%)
False261
65.2%
True137
34.2%
(Missing)2
 
0.5%
2021-06-05T12:47:00.940711image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

cad
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.5%
Missing2
Missing (%)0.5%
Memory size928.0 B
False
364 
True
 
34
(Missing)
 
2
ValueCountFrequency (%)
False364
91.0%
True34
 
8.5%
(Missing)2
 
0.5%
2021-06-05T12:47:00.994567image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

appet
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.5%
Missing1
Missing (%)0.2%
Memory size23.9 KiB
good
317 
poor
82 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1596
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgood
2nd rowgood
3rd rowpoor
4th rowpoor
5th rowgood

Common Values

ValueCountFrequency (%)
good317
79.2%
poor82
 
20.5%
(Missing)1
 
0.2%

Length

2021-06-05T12:47:01.205999image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-05T12:47:01.289775image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
good317
79.4%
poor82
 
20.6%

Most occurring characters

ValueCountFrequency (%)
o798
50.0%
g317
 
19.9%
d317
 
19.9%
p82
 
5.1%
r82
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1596
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o798
50.0%
g317
 
19.9%
d317
 
19.9%
p82
 
5.1%
r82
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
Latin1596
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o798
50.0%
g317
 
19.9%
d317
 
19.9%
p82
 
5.1%
r82
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1596
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o798
50.0%
g317
 
19.9%
d317
 
19.9%
p82
 
5.1%
r82
 
5.1%

pe
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.5%
Missing1
Missing (%)0.2%
Memory size928.0 B
False
323 
True
76 
(Missing)
 
1
ValueCountFrequency (%)
False323
80.8%
True76
 
19.0%
(Missing)1
 
0.2%
2021-06-05T12:47:01.340640image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

ane
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.5%
Missing1
Missing (%)0.2%
Memory size928.0 B
False
339 
True
60 
(Missing)
 
1
ValueCountFrequency (%)
False339
84.8%
True60
 
15.0%
(Missing)1
 
0.2%
2021-06-05T12:47:01.390506image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

class
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size24.0 KiB
ckd
250 
notckd
150 

Length

Max length6
Median length3
Mean length4.125
Min length3

Characters and Unicode

Total characters1650
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowckd
2nd rowckd
3rd rowckd
4th rowckd
5th rowckd

Common Values

ValueCountFrequency (%)
ckd250
62.5%
notckd150
37.5%

Length

2021-06-05T12:47:01.595957image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-05T12:47:01.679733image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
ckd250
62.5%
notckd150
37.5%

Most occurring characters

ValueCountFrequency (%)
c400
24.2%
k400
24.2%
d400
24.2%
n150
 
9.1%
o150
 
9.1%
t150
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1650
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c400
24.2%
k400
24.2%
d400
24.2%
n150
 
9.1%
o150
 
9.1%
t150
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
Latin1650
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
c400
24.2%
k400
24.2%
d400
24.2%
n150
 
9.1%
o150
 
9.1%
t150
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1650
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c400
24.2%
k400
24.2%
d400
24.2%
n150
 
9.1%
o150
 
9.1%
t150
 
9.1%

Interactions

2021-06-05T12:46:30.071869image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:30.195450image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:30.311141image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:30.425834image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:30.539531image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:30.655221image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:30.787191image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:30.918803image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:31.050416image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:31.159595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:31.283031image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:31.392272image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:31.495597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:31.615133image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:31.738685image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:31.880543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:32.103800image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:32.235443image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:32.367042image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:32.508785image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:32.668327image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:32.801837image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:32.923333image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:33.063041image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:33.186601image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:33.308145image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:33.439771image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:33.561301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:33.692888image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:33.814440image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:33.936141image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:34.085922image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:34.219578image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:34.351144image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:34.470599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:34.584022image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:34.713602image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:34.826974image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:34.948406image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:35.069859image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:35.191263image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:35.322897image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:35.444340image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:35.565887image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:35.687302image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:35.829084image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:35.959018image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:36.082559image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:36.193927image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:36.325562image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:36.447010image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:36.568484image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:36.810154image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:36.934016image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:37.065684image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:37.205430image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:37.326906image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:37.450480image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:37.592244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:37.713759image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:37.835237image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:37.957048image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:38.088640image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:38.210132image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:38.331647image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:38.451042image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:38.584733image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:38.736651image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:38.878320image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:39.020048image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:39.161769image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:39.321681image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:39.475847image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:39.627636image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:39.759273image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:39.891223image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:40.020825image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:40.144388image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:40.276008image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:40.407685image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:40.549446image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:40.681006image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:40.802219image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:40.924320image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:41.076187image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:41.227980image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:41.369666image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:41.491265image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:41.643116image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:41.772665image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:41.904486image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:42.038064image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:42.179850image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:42.321600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-06-05T12:46:42.706259image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:42.835925image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:42.989916image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:43.131605image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:43.291489image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-06-05T12:46:43.546594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:43.678164image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:43.809814image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:43.969991image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:44.073273image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:44.184566image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:44.295886image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:44.407264image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:44.518584image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:44.650219image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:44.769652image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:44.883705image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:44.986146image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:45.098661image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:45.200976image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:45.303278image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:45.415794image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:45.538682image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:45.671612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:45.810762image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:45.940421image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:46.081008image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:46.227616image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:46.377216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:46.516879image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:46.636559image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:46.775188image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:46.899855image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:47.026515image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:47.158167image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:47.267871image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:47.386556image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:47.498256image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:47.610954image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:47.724648image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:47.843332image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:47.966005image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:48.087679image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:48.192411image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:48.314075image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:48.423781image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:48.533488image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:48.649179image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:48.758885image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:48.878578image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:48.991262image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:49.102970image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:49.216663image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:49.335344image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:49.631556image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:49.756219image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:49.861936image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:49.983609image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:50.095311image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:50.217947image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:50.344608image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:50.469275image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:50.607904image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:50.740550image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:50.873195image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:51.002849image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:51.142475image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:51.283100image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:51.424720image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:51.549387image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:51.692006image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:51.821659image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-05T12:46:51.946363image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-06-05T12:47:01.773520image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-06-05T12:47:01.996887image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-06-05T12:47:02.246221image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-06-05T12:47:02.511511image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-06-05T12:47:02.844618image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-06-05T12:46:52.221626image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-06-05T12:46:52.884852image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-06-05T12:46:53.994290image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-06-05T12:46:54.706387image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

agebpsgalsurbcpcpccbabgrbuscsodpothemopcvwbcccrbcchtndmcadappetpeaneclass
048.080.01.0210NaNnormalnotpresentnotpresent121.036.01.2NaNNaN15.444.07800.05.2yesyesnogoodnonockd
17.050.01.0240NaNnormalnotpresentnotpresentNaN18.00.8NaNNaN11.338.06000.0NaNnononogoodnonockd
262.080.01.0123normalnormalnotpresentnotpresent423.053.01.8NaNNaN9.631.07500.0NaNnoyesnopoornoyesckd
348.070.01.00540normalabnormalpresentnotpresent117.056.03.8111.02.511.232.06700.03.9yesnonopooryesyesckd
451.080.01.0120normalnormalnotpresentnotpresent106.026.01.4NaNNaN11.635.07300.04.6nononogoodnonockd
560.090.01.01530NaNNaNnotpresentnotpresent74.025.01.1142.03.212.239.07800.04.4yesyesnogoodyesnockd
668.070.01.0100NaNnormalnotpresentnotpresent100.054.024.0104.04.012.436.0NaNNaNnononogoodnonockd
724.0NaN1.01524normalabnormalnotpresentnotpresent410.031.01.1NaNNaN12.444.06900.05.0noyesnogoodyesnockd
852.0100.01.01530normalabnormalpresentnotpresent138.060.01.9NaNNaN10.833.09600.04.0yesyesnogoodnoyesckd
953.090.01.0220abnormalabnormalpresentnotpresent70.0107.07.2114.03.79.529.012100.03.7yesyesnopoornoyesckd

Last rows

agebpsgalsurbcpcpccbabgrbuscsodpothemopcvwbcccrbcchtndmcadappetpeaneclass
39052.080.01.02500normalnormalnotpresentnotpresent99.025.00.8135.03.715.052.06300.05.3nononogoodnononotckd
39136.080.01.02500normalnormalnotpresentnotpresent85.016.01.1142.04.115.644.05800.06.3nononogoodnononotckd
39257.080.01.0200normalnormalnotpresentnotpresent133.048.01.2147.04.314.846.06600.05.5nononogoodnononotckd
39343.060.01.02500normalnormalnotpresentnotpresent117.045.00.7141.04.413.054.07400.05.4nononogoodnononotckd
39450.080.01.0200normalnormalnotpresentnotpresent137.046.00.8139.05.014.145.09500.04.6nononogoodnononotckd
39555.080.01.0200normalnormalnotpresentnotpresent140.049.00.5150.04.915.747.06700.04.9nononogoodnononotckd
39642.070.01.02500normalnormalnotpresentnotpresent75.031.01.2141.03.516.554.07800.06.2nononogoodnononotckd
39712.080.01.0200normalnormalnotpresentnotpresent100.026.00.6137.04.415.849.06600.05.4nononogoodnononotckd
39817.060.01.02500normalnormalnotpresentnotpresent114.050.01.0135.04.914.251.07200.05.9nononogoodnononotckd
39958.080.01.02500normalnormalnotpresentnotpresent131.018.01.1141.03.515.853.06800.06.1nononogoodnononotckd